stillleben generates realistic arrangements of rigid bodies and provides various outputs that can be used to train deep learning models.
For more information, we refer to the project homepage: https://AIS-Bonn.github.io/stillleben/
stillleben is developed by Max Schwarz (max.schwarz@ais.uni-bonn.de), with differentiation support added by Arul Selvam Periyasamy (periyasamy@ais.uni-bonn.de).
stillleben is licensed under the MIT license (see LICENSE). It is built on top of the following third-party modules:
- Magnum 3D Engine (MIT),
- PhysX physics engine (BSD-3),
- V-HACD (included in
contrib/v-hacd, BSD-3) - imgui (included in
contrib/imgui, MIT)
If you use stillleben in scientific work, please consider citing:
Max Schwarz and Sven Behnke:
Stillleben: Realistic Scene Synthesis for Deep Learning in Robotics
IEEE International Conference on Robotics and Automation (ICRA), May 2020,
and for differentiation support:
Arul Selvam Periyasamy, Max Schwarz, and Sven Behnke:
Refining 6D Object Pose Predictions using Abstract Render-and-Compare
IEEE-RAS International Conference on Humanoid Robots (Humanoids), October 2019.